A quantile regression perspective on external preference mapping

AStA Advances in Statistical Analysis - External preference mapping is widely used in marketing and R&D divisions to understand the consumer behaviour. The most common preference map is...     

Subjective Economic Status in Adolescence: Determinants and Associations with Mental Health in the Norwegian Youth@Hordaland Study

We aimed to identify factors associated with perceived economic well-being (PEWB), and examine its association with symptoms of depression and attention-deficit/hyperactivity disorder. In the Norwegian youth@hordaland study, 9166 16–19-year-olds provided information on perceived economic well-being and relevant covariates. Information about families’ income-to-needs was obtained from tax return forms. Adolescents in households with a low income-to-needs ratio, with non-working parents, and in single-parent households were more likely to report poor PEWB. Adolescents with poor PEWB reported more symptoms of depression and ADHD, also after adjusting for covariates, including income-to-needs. There was a significant indirect effect of income-to-needs on mental health problems though PEWB. The current study demonstrates the role of PEWB as a contributor in the pathway from social inequalities to disparities in mental health.     

The promise and pitfalls of composite endpoints in sepsis and COVID-19 clinical trials

Composite endpoints reveal the tendency for statistical convention to arise locally within subfields. Composites are familiar in cardiovascular trials, yet almost unknown in sepsis. However, the VITAMINS trial in patients with septic shock adopted a composite of mortality and vasopressor-free days, and an ordinal scale describing patient status rapidly became standard in COVID studies. Aware that recent use could incite interest in such endpoints, we are motivated to flag their potential value and pitfalls for sepsis research and COVID studies.     

Analyzing Designed Experiments with Multiple Responses

This paper is an overview of a unified framework for analyzing designed experiments with univariate or multivariate responses. Both categorical and continuous design variables are considered. To handle unbalanced data, we introduce the so-called Type II* sums of squares. This means that the results are independent of the scale chosen for continuous design variables. Furthermore, it does not matter whether two-level variables are coded as categorical or continuous. Overall testing of all responses is done by 50-50 MANOVA, which handles several highly correlated responses. Univariate p-values for each response are adjusted by using rotation testing. To illustrate multivariate effects, mean values and mean predictions are illustrated in a principal component score plot or directly as curves. For the unbalanced cases, we introduce a new variant of adjusted means, which are independent to the coding of two-level variables. The methodology is exemplified by case studies from cheese and fish pudding production.     

Divorce and Family Structure in Norway: Associations With Adolescent Mental Health

The aim of this study was to examine the association between family structure and adolecent mental health, after the considerable increase in divorced parents choosing joint physical custody (JPC) in Norway. Data stem from the youth@hordaland study, a population-based survey conducted in Norway in 2012. A total of 7,707 adolescents (47% male) 16 to 19 years old were included in this study. The adolescents were classified into 6 family structures. Mental health was measured using the Strengths and Difficulties Questionnaire (SDQ). No significant differences between nondivorced families (reference) and JPC were observed. Adolescents from single-parent and stepfather families scored significantly higher on all 3 SDQ scales, and adolescents from stepmother families scored significantly higher on the SDQ total and SDQ externalizing scales. In conclusion, the results of this study indicated that adolescents living in JPC did not have more adjustment problems compared to their peers living in nondivorced families.     

Comparison of prediction methods for multicollinear data

In this paper we discuss the partial least squares (PLS) prediction method. The method is compared to the predictor based on principal component regression (PCR). Both theoretical considerations and computations on artificial and real data are presented.